Notional (intended) activities such as movements, speech, emotions or visual imaginations can be derived using neurobiological methods from the brain activity of human beings and can be reconstructed by approximation. Detection systems known from prior art allow the spatio-temporal detection of the brain activities of a human being in order to thereby derive signals representative of the intended movement, speech, emotion or visual imagination. For example, foil electrodes or depth electrodes can be provided which preferably detect with a high temporal resolution, approximately within the range between 1 Hz and 1000 Hz, the brain activities and provide them as electrical signals for further processing.
Known methods and methods for the reconstruction of movements, speech, etc. from brain activities are described in “Brain-Machine Interface Engineering (Synthesis Lectures on Biomedical Engineering); Justin C. Sanchez, at al.; Morgan & Claypool Publishers, 1st Edition, November 2006”, “Toward Brain-computer interfacing (Neural information Processing); Guido Dornhege et al.; The MIT Press, 1st edition, July 2007” and “Brain-computer Interfaces: An international assessment of research and development trends; Theodore W. Berger et al.; Springer Netherlands, 1st edition, November 2008”.
With the devices for deriving brain activities known from prior art, the latter are measured and passed onto an evaluation unit, for example, to a computer system. For this purpose, standard appliances such as magnetic resonance tomographs (MRI) are used, which measure the local brain activity indirectly by the consumption of oxygen (functional MRI). Furthermore, electrophysiological devices can be used to measure the local brain currents (electroencephalography, electrocorticography, measurement of local field potentials or measurement of single-cell activities by intra-cortical electrodes), amplify them, and then send them to the evaluation unit.
The reconstruction is performed by means of a model for translation of the measured brain activities into movements, sound/words, emotions, or images [in the following in summary referred to as states or activities], which usually operates in the evaluation unit. The model is usually a mathematical algorithm which converts the measured brain activities in real time using a data processing means (computer and/or embedded system) into states/activities.
For the generation of the model, various methods are known. For example, methods known from signal analysis, such as “reverse correlation” or adaptation of, mostly linear filters, can be used to generate the model of the signal analysis.
The models, on the one hand, reflect the function of the measured brain activities, or naive, are calculated without assumptions about the function of the measured brain activities, by linking these to various preset training stimuli, or can be created by a combination of both. In the end, the model stands as a translator for the measured brain activities into the respectively selected states/activities.
A disadvantage of the reconstruction methods known from prior art is the high fuzziness or inaccuracy, respectively, of the reconstruction results. This has several reasons that cannot be overcome by the known methods.
With functional magnetic resonance tomography, the activity can be measured in all areas of the brain, however, only with a very poor temporal resolution in the range of seconds—while the brain operates is in the range of milliseconds or less. Although the spatial resolution of the brain activity is good and is constantly being improved, it nevertheless has a magnitude in the range of cubic millimeters—while the functional structures in the brain have a magnitude in the range of a few micrometers.
Electro-physiological measurement methods have similar shortcomings. With these—also for measurements with a high number of electrodes (several hundred)—only a fraction of the brain activity of selected areas can be recorded. Due to technical reasons, therefore, all measurements can only detect incomplete brain activities.
A further issue is the inaccuracy of the measurement. Brain activities have very low current flows which have to be measured under difficult conditions (in living tissue in a moving body). This means that the signals cannot be measured accurately and are usually contaminated with artifacts.
A further factor for the inaccuracy or fuzziness of the measurements is the fact that the brain activity for one and the same stimulus can be different. This phenomenon is either referred to as an intrinsic noise (“neural noise”), or as a physiologically relevant activity due to the ongoing and parallel processing of various states (see “Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses; Arieli A. et al.; Science, 27 Sep. 1996, pages 1868-1871”).
These features for the detection of brain activity entail always being subject to uncertainty/fuzziness. As a result, for example, a correlation coefficient is obtained within the range of 0.5 for the reconstruction of movements, or decoding accuracy for vowels only within the range of 80%.